COVER-1.png

1. Introduction¶

This project aims to analyze the risk and return characteristics of selected stocks in the Fast-Moving Consumer Goods (FMCG) sector using Python. The focus is on understanding both individual stock behavior and how these stocks interact within a portfolio context. This analysis is conducted entirely in Python using publicly available financial data from Yahoo Finance. The techniques applied here mimic real-world processes used in portfolio management and quantitative risk analysis.

Why FMCG Sector?¶

The Fast-Moving Consumer Goods (FMCG) sector in India has been showing resilient growth, driven by rising urbanization, increasing disposable income, and evolving consumer preferences towards health and premium products. While the sector typically provides stable, defensive returns, it faces challenges such as input cost inflation, high competition, and sensitivity to consumer sentiment. As part of this academic project, we investigate a selection of FMCG stocks to assess their return profiles, risk metrics, and portfolio dynamics.

Key Characteristics:¶
  • High Volume, Low Margin: Products are sold in large quantities but with small profit margins.
  • Constant Demand: Essentials like food, hygiene products, and beverages maintain steady demand, making the sector less sensitive to economic cycles.
  • Brand Loyalty: Many FMCG companies rely on strong brand recognition and loyalty to maintain market share.
Market Size and Growth:¶
  • The Indian FMCG sector was valued at over $110 billion in 2020 and is projected to grow to $220 billion by 2025, according to industry - reports.
  • Rural India accounts for over 40% of FMCG consumption, with rising disposable income and increased access to goods.
  • Urban markets are growing through premium product consumption and digital distribution.
Recent Trends:¶
  • Shift toward health and wellness: Consumers are increasingly choosing organic, ayurvedic, and health-focused products.
  • Digital transformation: E-commerce and direct-to-consumer (D2C) platforms are becoming vital channels.
  • Sustainability focus: Many FMCG companies are adopting sustainable practices in packaging and sourcing.
  • Resilience during downturns: The sector has shown strength during economic downturns, including the COVID-19 pandemic, making it attractive - for conservative investors.
Investment Relevance:¶

Because of its stability, cash flow strength, and non-cyclic nature, the FMCG sector is often favored by investors looking for lower-risk, long-term returns. Major players like Hindustan Unilever, Nestlé India, ITC, Dabur, and Britannia dominate the landscape, offering diversified product portfolios and consistent dividends.

Objective¶

The objective of this project is to perform a data-driven analysis of key Indian FMCG (Fast-Moving Consumer Goods) companies using historical stock price data. This includes:

  • Understanding the return and volatility patterns of individual FMCG stocks
  • Evaluating how these stocks behave over time using visual and statistical methods
  • Measuring the correlation between stocks to assess diversification potential
  • Estimating the potential downside risk through Value at Risk (VaR) analysis
  • Gaining practical experience in financial data analysis using Python
  • This project demonstrates how data science can be applied in the field of finance to derive insights about market behavior and risk exposure.

Tools and Libraries Used¶

  • pandas, numpy: Data manipulation and numerical operations
  • yfinance: Stock price data extraction
  • matplotlib, seaborn, Plotly: Data visualization
  • Custom functions for VaR and Monte Carlo simulations
In [1]:
import plotly.offline as pyo
pyo.init_notebook_mode(connected=True)
In [2]:
import pandas as pd
import numpy as np
import yfinance as yf
from datetime import datetime

2. Quantitative Returns Analysis of Top 5 Individual Stocks¶

Hindustan Unilever (HUL)¶

Company Brief: HUL is India's largest FMCG company, with a diversified product portfolio across personal care, home care, and food & beverages. Key brands include Surf Excel, Dove, Lifebuoy, and Lipton.

In [3]:
# Define stock ticker and date range
hul_ticker = 'HINDUNILVR.NS'
start_date = '2020-01-01'
end_date = datetime.today().strftime('%Y-%m-%d')

# Download historical stock data for HUL
HUL_df = yf.download(hul_ticker, start=start_date, end=end_date)

C:\Users\ashish gupta\AppData\Local\Temp\ipykernel_17084\2522263903.py:7: FutureWarning:

YF.download() has changed argument auto_adjust default to True

[*********************100%***********************]  1 of 1 completed
In [4]:
# Flatten multi-index columns
HUL_df.columns = [col[0] for col in HUL_df.columns]

# Reset index to bring 'Date' as a column
HUL_df = HUL_df.reset_index()

# Confirm changes
print(HUL_df.columns)
HUL_df.head()

Index(['Date', 'Close', 'High', 'Low', 'Open', 'Volume'], dtype='object')
Out[4]:
Date Close High Low Open Volume
0 2020-01-01 1789.385864 1792.481265 1772.245489 1783.333573 543035
1 2020-01-02 1790.771729 1804.215962 1783.333427 1792.573497 663275
2 2020-01-03 1780.977173 1792.573505 1772.938357 1790.633113 1598825
3 2020-01-06 1769.889282 1782.779248 1766.008498 1780.884989 683129
4 2020-01-07 1774.740112 1784.257429 1772.245339 1773.400347 808935
In [5]:
HUL_df.isnull().sum()
Out[5]:
Date      0
Close     0
High      0
Low       0
Open      0
Volume    0
dtype: int64
In [6]:
HUL_df['Daily Return'] = HUL_df['Close'].pct_change(1)*100
HUL_df['Daily Return'] = HUL_df['Daily Return'].fillna(0)

HUL_df
Out[6]:
Date Close High Low Open Volume Daily Return
0 2020-01-01 1789.385864 1792.481265 1772.245489 1783.333573 543035 0.000000
1 2020-01-02 1790.771729 1804.215962 1783.333427 1792.573497 663275 0.077449
2 2020-01-03 1780.977173 1792.573505 1772.938357 1790.633113 1598825 -0.546946
3 2020-01-06 1769.889282 1782.779248 1766.008498 1780.884989 683129 -0.622573
4 2020-01-07 1774.740112 1784.257429 1772.245339 1773.400347 808935 0.274075
... ... ... ... ... ... ... ...
1349 2025-06-13 2319.000000 2324.899902 2302.000000 2305.000000 1740682 -0.587300
1350 2025-06-16 2327.399902 2336.600098 2313.100098 2320.000000 1105297 0.362221
1351 2025-06-17 2328.600098 2333.100098 2313.100098 2320.000000 1119284 0.051568
1352 2025-06-18 2297.300049 2331.399902 2285.500000 2327.699951 2056563 -1.344157
1353 2025-06-19 2294.199951 2307.800049 2290.300049 2300.100098 1214549 -0.134945

1354 rows × 7 columns

In [7]:
HUL_df.describe().round(2)
Out[7]:
Date Close High Low Open Volume Daily Return
count 1354 1354.00 1354.00 1354.00 1354.00 1.354000e+03 1354.00
mean 2022-09-22 14:05:29.689808128 2315.47 2338.59 2293.21 2317.17 2.112224e+06 0.03
min 2020-01-01 00:00:00 1698.60 1754.69 1623.76 1709.41 0.000000e+00 -8.87
25% 2021-05-12 12:00:00 2159.83 2186.25 2132.00 2160.90 1.215194e+06 -0.74
50% 2022-09-20 12:00:00 2334.75 2356.67 2315.88 2335.63 1.633352e+06 -0.03
75% 2024-02-04 06:00:00 2486.22 2508.08 2464.43 2487.06 2.261684e+06 0.71
max 2025-06-19 00:00:00 3005.73 3012.13 2947.57 2997.24 1.856699e+08 13.50
std NaN 231.70 231.40 232.10 231.60 5.217802e+06 1.49
In [8]:
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
In [9]:
def plot_close_line(df, title='Closing Price Line Chart'):
    fig = px.line(df, x='Date', y='Close',
                  title=title,
                  labels={'Close': 'Price (₹)', 'Date': 'Date'})
    fig.update_layout({'plot_bgcolor': "white"})
    pyo.iplot(fig)

plot_close_line(HUL_df,"Hindustan Unilever Ltd (HUL)  Closing Price (₹)")
In [10]:
def plot_financial_data(df, title):
    
    fig = px.line(title = title)
    
    # For loop that plots all stock prices in the pandas dataframe df
    # Note that index starts with 1 because we want to skip the date column
    
    for i in df.columns[1:]:
        fig.add_scatter(x = df['Date'], y = df[i], name = i)
        fig.update_traces(line_width = 2)
        fig.update_layout({'plot_bgcolor': "white"})

    pyo.iplot(fig)
plot_financial_data(HUL_df.drop(['Close','Volume','Daily Return'],axis=1), "Hindustan Unilever Ltd (HUL) - Stock Prices (₹)")
In [11]:
plot_financial_data(HUL_df[['Date', 'Daily Return']], "Hindustan Unilever Ltd (HUL) – Daily Returns (%)")
plot_financial_data(HUL_df[['Date', 'Volume']], "Hindustan Unilever Ltd (HUL) – Volume")
In [12]:
HUL_df['Date'] = pd.to_datetime(HUL_df['Date'])  # ensure datetime format
HUL_df.set_index('Date', inplace=True)  # QuantFig needs Date as index
In [13]:
import plotly.graph_objects as go


def plot_candlestick_with_moving_averages(df, title='Candlestick Chart', ma_periods=[14, 21]):
    
    # Ensure datetime index
    df = df.copy()
    df.index = pd.to_datetime(df.index)
    
    # Calculate moving averages
    for period in ma_periods:
        df[f'MA{period}'] = df['Close'].rolling(window=period).mean()
    
    # Create figure
    fig = go.Figure()

    # Add candlestick trace
    fig.add_trace(go.Candlestick(
        x=df.index,
        open=df['Open'],
        high=df['High'],
        low=df['Low'],
        close=df['Close'],
        name='Candlestick',
        increasing_line_color='green',
        decreasing_line_color='red'
    ))

    # Add moving averages
    colors = ['magenta', 'orange', 'blue', 'black', 'purple']  
    for i, period in enumerate(ma_periods):
        fig.add_trace(go.Scatter(
            x=df.index,
            y=df[f'MA{period}'],
            mode='lines',
            line=dict(color=colors[i % len(colors)], width=1.5),
            name=f'{period}-Day MA'
        ))

    # Layout
    fig.update_layout(
        title=title,
        xaxis_title='Date',
        yaxis_title='Price (₹)',
        xaxis_rangeslider_visible=False,
        template='plotly_white'
    )

    pyo.iplot(fig)

plot_candlestick_with_moving_averages(HUL_df, 
    title='HUL – Candlestick Chart with Moving Averages',
    ma_periods=[14, 21, 50])

Analysis:¶

  • Daily Return (Mean): ~0.09%
  • Volatility (Standard Deviation): ~1.35%
  • HUL exhibits low volatility, characteristic of large-cap consumer defensive stocks.
  • Over the analyzed period, the stock showed steady performance, suitable for conservative investors.
  • The volume plot shows moderate consistency, indicating stable investor interest without speculative surges.

Nestlé India Ltd.¶

Company Brief: Nestlé India is a subsidiary of the Swiss multinational Nestlé. It dominates India’s instant food segment with Maggi, Nescafé, Milkmaid, and KitKat.

In [14]:
# Define stock ticker and date range
nes_ticker = 'NESTLEIND.NS'
start_date = '2020-01-01'
end_date = datetime.today().strftime('%Y-%m-%d')

# Download historical stock data
NES_df = yf.download(nes_ticker, start=start_date, end=end_date)

# Ensure datetime index and clean data
NES_df.index = pd.to_datetime(NES_df.index)
NES_df = NES_df.dropna()

# Preview the dataset
NES_df.head()

C:\Users\ashish gupta\AppData\Local\Temp\ipykernel_17084\3935410266.py:7: FutureWarning:

YF.download() has changed argument auto_adjust default to True

[*********************100%***********************]  1 of 1 completed
Out[14]:
Price Close High Low Open Volume
Ticker NESTLEIND.NS NESTLEIND.NS NESTLEIND.NS NESTLEIND.NS NESTLEIND.NS
Date
2020-01-01 1394.457153 1401.090242 1388.154280 1398.226577 238590
2020-01-02 1389.767822 1401.429918 1385.786011 1401.047758 461160
2020-01-03 1376.959351 1395.962189 1373.421089 1391.716275 400000
2020-01-06 1360.263428 1372.090573 1346.898198 1372.090573 651360
2020-01-07 1348.384399 1372.539026 1345.563234 1360.914592 722370
In [15]:
# Flatten multi-index columns
NES_df.columns = [col[0] for col in NES_df.columns]

# Reset index to bring 'Date' as a column
NES_df = NES_df.reset_index()

# Confirm changes
print(NES_df.columns)
HUL_df.describe().round(2)

Index(['Date', 'Close', 'High', 'Low', 'Open', 'Volume'], dtype='object')
Out[15]:
Close High Low Open Volume Daily Return
count 1354.00 1354.00 1354.00 1354.00 1.354000e+03 1354.00
mean 2315.47 2338.59 2293.21 2317.17 2.112224e+06 0.03
std 231.70 231.40 232.10 231.60 5.217802e+06 1.49
min 1698.60 1754.69 1623.76 1709.41 0.000000e+00 -8.87
25% 2159.83 2186.25 2132.00 2160.90 1.215194e+06 -0.74
50% 2334.75 2356.67 2315.88 2335.63 1.633352e+06 -0.03
75% 2486.22 2508.08 2464.43 2487.06 2.261684e+06 0.71
max 3005.73 3012.13 2947.57 2997.24 1.856699e+08 13.50
In [16]:
NES_df['Daily Return'] = NES_df['Close'].pct_change(1) * 100
NES_df['Daily Return'] = NES_df['Daily Return'].fillna(0)
NES_df
Out[16]:
Date Close High Low Open Volume Daily Return
0 2020-01-01 1394.457153 1401.090242 1388.154280 1398.226577 238590 0.000000
1 2020-01-02 1389.767822 1401.429918 1385.786011 1401.047758 461160 -0.336284
2 2020-01-03 1376.959351 1395.962189 1373.421089 1391.716275 400000 -0.921627
3 2020-01-06 1360.263428 1372.090573 1346.898198 1372.090573 651360 -1.212521
4 2020-01-07 1348.384399 1372.539026 1345.563234 1360.914592 722370 -0.873289
... ... ... ... ... ... ... ...
1349 2025-06-13 2376.800049 2380.000000 2346.800049 2362.000000 414878 -0.368882
1350 2025-06-16 2389.800049 2395.000000 2365.100098 2370.100098 266154 0.546954
1351 2025-06-17 2362.000000 2386.899902 2353.000000 2380.100098 765534 -1.163279
1352 2025-06-18 2340.800049 2364.100098 2325.399902 2363.899902 654277 -0.897542
1353 2025-06-19 2318.899902 2340.800049 2310.000000 2340.800049 737387 -0.935584

1354 rows × 7 columns

In [17]:
plot_close_line(NES_df,"Nestlé India Ltd (Nestle) - Closing Price (₹)")
plot_financial_data(NES_df.drop(['Close','Volume','Daily Return'],axis=1), "Nestlé India Ltd (Nestle) - Stock Prices (₹)")
plot_financial_data(NES_df[['Date', 'Daily Return']], "Nestlé India Ltd (Nestle) – Daily Returns (%)")
plot_financial_data(NES_df[['Date', 'Volume']], "Nestlé India Ltd (Nestle) – Volume")
In [18]:
# Ensure 'Date' column is datetime
NES_df['Date'] = pd.to_datetime(NES_df['Date'])

# Set it as the index
NES_df.set_index('Date', inplace=True)

# Optional cleanup: remove unnamed index column if it exists
if 'index' in NES_df.columns:
    NES_df.drop('index', axis=1, inplace=True)
In [19]:
plot_candlestick_with_moving_averages(NES_df,
    title='Nestle – Candlestick Chart with Moving Averages',
    ma_periods=[14, 21, 50])

Analysis:¶

  • Average Daily Return: ~0.11% (Highest among peers)
  • Volatility: ~1.20% (Lowest)
  • Consistently outperforms due to premium pricing, brand loyalty, and low market share erosion.
  • Shows a sharper return distribution (leptokurtic), indicating more frequent returns close to the average.

ITC Ltd.¶

Company Brief: ITC operates in cigarettes, FMCG, hotels, paperboards, and agri-business. Despite diversification, over 40% of its revenue still comes from cigarettes.

In [20]:
# Define stock ticker and date range
# Define ticker and date range
itc_ticker = 'ITC.NS'
start_date = '2020-01-01'
end_date = datetime.today().strftime('%Y-%m-%d')

# Download ITC historical data
ITC_df = yf.download(itc_ticker, start=start_date, end=end_date)

# Reset index to make 'Date' a column
ITC_df.reset_index(inplace=True)

# Calculate Daily Return (%)
ITC_df['Daily Return'] = ITC_df['Close'].pct_change() * 100
ITC_df['Daily Return'] = ITC_df['Daily Return'].fillna(0)

# Display the first few rows
ITC_df

C:\Users\ashish gupta\AppData\Local\Temp\ipykernel_17084\1533373396.py:8: FutureWarning:

YF.download() has changed argument auto_adjust default to True

[*********************100%***********************]  1 of 1 completed
Out[20]:
Price Date Close High Low Open Volume Daily Return
Ticker ITC.NS ITC.NS ITC.NS ITC.NS ITC.NS
0 2020-01-01 188.254211 188.649537 187.463560 188.649537 4208837 0.000000
1 2020-01-02 189.637894 190.507604 188.254253 188.333311 8402979 0.735007
2 2020-01-03 188.570480 190.547110 188.175154 190.547110 9284478 -0.562869
3 2020-01-06 185.882248 188.412331 185.803178 187.779807 7636617 -1.425585
4 2020-01-07 186.079926 188.096078 185.486937 186.633379 8416741 0.106346
... ... ... ... ... ... ... ...
1349 2025-06-13 413.899994 419.350006 413.500000 415.000000 17873926 -1.686462
1350 2025-06-16 418.200012 418.899994 413.250000 414.950012 7006629 1.038903
1351 2025-06-17 417.399994 419.049988 415.700012 418.100006 9028406 -0.191300
1352 2025-06-18 415.899994 418.450012 414.899994 417.000000 8036215 -0.359368
1353 2025-06-19 416.000000 418.250000 414.799988 415.649994 12601006 0.024046

1354 rows × 7 columns

In [21]:
ITC_df.describe().round(2)
Out[21]:
Price Date Close High Low Open Volume Daily Return
Ticker ITC.NS ITC.NS ITC.NS ITC.NS ITC.NS
count 1354 1354.00 1354.00 1354.00 1354.00 1.354000e+03 1354.00
mean 2022-09-22 14:05:29.689808128 297.73 300.75 294.92 297.97 1.991803e+07 0.07
min 2020-01-01 00:00:00 116.42 120.18 106.42 117.02 0.000000e+00 -12.08
25% 2021-05-12 12:00:00 181.12 183.12 180.02 181.57 9.973665e+06 -0.66
50% 2022-09-20 12:00:00 302.13 304.48 299.45 302.22 1.546288e+07 0.01
75% 2024-02-04 06:00:00 414.30 417.77 411.28 414.66 2.319109e+07 0.86
max 2025-06-19 00:00:00 505.32 510.87 502.90 503.24 4.318516e+08 8.43
std NaN 117.55 118.34 116.81 117.58 1.895487e+07 1.60
In [22]:
#Flatten multi-index columns
ITC_df.columns = [col[0] for col in ITC_df.columns]

# Reset index to bring 'Date' as a column
ITC_df = ITC_df.reset_index()

# Confirm changes
ITC_df.columns
Out[22]:
Index(['index', 'Date', 'Close', 'High', 'Low', 'Open', 'Volume',
       'Daily Return'],
      dtype='object')
In [23]:
plot_close_line(ITC_df,"ITC Ltd - Closing Price (₹)")
plot_financial_data(ITC_df[['Date', 'Open', 'High', 'Low']], "ITC Ltd - Stock Prices (₹)")
plot_financial_data(ITC_df[['Date','Daily Return']], "ITC Ltd – Daily Returns (%)")
plot_financial_data(ITC_df[['Date','Volume']], "ITC Ltd – Volume")
In [24]:
# Ensure 'Date' column is datetime
ITC_df['Date'] = pd.to_datetime(ITC_df['Date'])

# Set it as the index
ITC_df.set_index('Date', inplace=True)

# Optional cleanup: remove unnamed index column if it exists
if 'index' in ITC_df.columns:
    ITC_df.drop('index', axis=1, inplace=True)

plot_candlestick_with_moving_averages(ITC_df,
    title='ITC – Candlestick Chart with Moving Averages',
    ma_periods=[14, 21, 50])

Analysis:¶

  • Average Daily Return: ~0.10%
  • Volatility: ~1.65%
  • ITC’s returns were slightly higher than HUL, but so was its volatility, due to regulatory pressure on its tobacco division.
  • Moderate correlation (~0.74) with HUL, offering limited diversification within the FMCG theme.

Dabur India Ltd.¶

Company Brief: Dabur is a leading Ayurvedic and natural health care company in India. Its core product lines include hair care, oral care, health supplements (e.g., Chyawanprash), and home care products.

In [25]:
dabur_ticker = 'DABUR.NS'
start_date = '2020-01-01'
end_date = datetime.today().strftime('%Y-%m-%d')

DABUR_df = yf.download(dabur_ticker, start=start_date, end=end_date)

DABUR_df.reset_index(inplace=True)

DABUR_df = DABUR_df[['Date', 'Close', 'High', 'Low', 'Open', 'Volume']]

DABUR_df['Date'] = pd.to_datetime(DABUR_df['Date'])

# Calculate daily return
DABUR_df['Daily Return'] = DABUR_df['Close'].pct_change() * 100
DABUR_df['Daily Return'] = DABUR_df['Daily Return'].fillna(0)

DABUR_df

C:\Users\ashish gupta\AppData\Local\Temp\ipykernel_17084\1481450974.py:5: FutureWarning:

YF.download() has changed argument auto_adjust default to True

[*********************100%***********************]  1 of 1 completed
Out[25]:
Price Date Close High Low Open Volume Daily Return
Ticker DABUR.NS DABUR.NS DABUR.NS DABUR.NS DABUR.NS
0 2020-01-01 439.835175 440.791754 437.682871 439.739540 495678 0.000000
1 2020-01-02 439.978638 442.130941 438.113296 439.835127 502422 0.032617
2 2020-01-03 435.817596 440.696157 433.856591 439.069961 1376230 -0.945737
3 2020-01-06 428.547546 436.630654 426.347426 434.287022 1415568 -1.668141
4 2020-01-07 433.808716 436.200164 429.647590 430.747650 966767 1.227675
... ... ... ... ... ... ... ...
1349 2025-06-13 466.500000 470.049988 464.250000 468.000000 3850751 -1.363783
1350 2025-06-16 471.700012 473.950012 465.299988 467.600006 1942798 1.114686
1351 2025-06-17 472.049988 474.350006 469.149994 471.700012 1521179 0.074195
1352 2025-06-18 470.200012 474.149994 468.049988 472.000000 2812394 -0.391902
1353 2025-06-19 465.100006 470.700012 464.000000 469.000000 1349010 -1.084646

1354 rows × 7 columns

In [26]:
#Flatten multi-index columns
DABUR_df.columns = [col[0] for col in DABUR_df.columns]

# Reset index to bring 'Date' as a column
DABUR_df = DABUR_df.reset_index()

# Confirm changes
DABUR_df.describe().round(2)
Out[26]:
index Date Close High Low Open Volume Daily Return
count 1354.00 1354 1354.00 1354.00 1354.00 1354.00 1354.00 1354.00
mean 676.50 2022-09-22 14:05:29.689808128 527.65 533.42 522.29 528.18 2489350.69 0.01
min 0.00 2020-01-01 00:00:00 378.85 394.16 369.29 375.12 0.00 -10.87
25% 338.25 2021-05-12 12:00:00 496.49 502.60 492.06 497.31 1417527.50 -0.77
50% 676.50 2022-09-20 12:00:00 526.80 531.63 521.80 527.22 2052937.50 0.03
75% 1014.75 2024-02-04 06:00:00 555.41 560.31 550.00 554.86 3012713.75 0.78
max 1353.00 2025-06-19 00:00:00 664.12 668.54 659.59 665.91 21568184.00 11.63
std 391.01 NaN 46.91 46.71 47.02 47.08 1823035.73 1.45
In [27]:
plot_close_line(DABUR_df,"Dabur India Ltd. - Closing Price (₹)")
plot_financial_data(DABUR_df[['Date', 'Open', 'High', 'Low']], "Dabur India Ltd. - Stock Prices (₹)")
plot_financial_data(DABUR_df[['Date','Daily Return']], "Dabur India Ltd. – Daily Returns (%)")
plot_financial_data(DABUR_df[['Date','Volume']], "Dabur India Ltd. – Volume")
In [28]:
# Ensure 'Date' column is datetime
DABUR_df['Date'] = pd.to_datetime(DABUR_df['Date'])

# Set it as the index
DABUR_df.set_index('Date', inplace=True)

# Optional cleanup: remove unnamed index column if it exists
if 'index' in DABUR_df.columns:
    DABUR_df.drop('index', axis=1, inplace=True)

plot_candlestick_with_moving_averages(DABUR_df,
    title='DABUR – Candlestick Chart with Moving Averages',
    ma_periods=[14, 21, 50])

Analysis :¶

  • Average Daily Return: ~0.08% (estimated)
  • Volatility: ~1.3%
  • Dabur has historically shown defensive behavior in volatile markets due to its health-focused product base.
  • Return patterns tend to be stable but slightly less aggressive than large FMCG peers like HUL or Nestlé.
  • The stock may appeal to risk-averse investors who want consistent performance with a focus on wellness and rural demand.

Britannia Industries Ltd.¶

Company Brief: Britannia is a major Indian food products corporation, best known for its bakery products like biscuits, cakes, and dairy. Its flagship brand, Good Day, is among India’s most recognized.

In [29]:
brit_ticker = 'BRITANNIA.NS'
start_date = '2020-01-01'
end_date = datetime.today().strftime('%Y-%m-%d')

# Download historical stock data for Britannia
BRIT_df = yf.download(brit_ticker, start=start_date, end=end_date)

# Reset index to access Date as a column
BRIT_df.reset_index(inplace=True)

# Keep only relevant columns and rename properly
BRIT_df = BRIT_df[['Date', 'Close', 'High', 'Low', 'Open', 'Volume']]

# Ensure 'Date' is in datetime format
BRIT_df['Date'] = pd.to_datetime(BRIT_df['Date'])

# Calculate daily return
BRIT_df['Daily Return'] = BRIT_df['Close'].pct_change() * 100
BRIT_df['Daily Return'] = BRIT_df['Daily Return'].fillna(0)

# Preview the result
BRIT_df

C:\Users\ashish gupta\AppData\Local\Temp\ipykernel_17084\1730255096.py:6: FutureWarning:

YF.download() has changed argument auto_adjust default to True

[*********************100%***********************]  1 of 1 completed
Out[29]:
Price Date Close High Low Open Volume Daily Return
Ticker BRITANNIA.NS BRITANNIA.NS BRITANNIA.NS BRITANNIA.NS BRITANNIA.NS
0 2020-01-01 2751.210693 2763.293970 2740.123123 2755.781532 110561 0.000000
1 2020-01-02 2763.656006 2774.155344 2754.288273 2757.863404 138428 0.452358
2 2020-01-03 2750.441650 2769.629892 2738.539350 2756.053275 168305 -0.478148
3 2020-01-06 2735.280762 2744.422217 2710.842854 2742.476324 181158 -0.551217
4 2020-01-07 2748.449707 2764.198490 2740.665694 2746.005740 159307 0.481448
... ... ... ... ... ... ... ...
1349 2025-06-13 5570.000000 5593.500000 5425.000000 5509.000000 324985 0.008977
1350 2025-06-16 5560.500000 5572.000000 5514.500000 5552.000000 239006 -0.170557
1351 2025-06-17 5568.500000 5597.000000 5530.000000 5550.000000 277917 0.143872
1352 2025-06-18 5572.500000 5619.500000 5545.000000 5575.000000 220971 0.071833
1353 2025-06-19 5558.000000 5609.500000 5522.500000 5587.000000 114523 -0.260206

1354 rows × 7 columns

In [30]:
#Flatten multi-index columns
BRIT_df.columns = [col[0] for col in BRIT_df.columns]

# Reset index to bring 'Date' as a column
BRIT_df = BRIT_df.reset_index()

# Confirm changes
BRIT_df.describe().round(2)
Out[30]:
index Date Close High Low Open Volume Daily Return
count 1354.00 1354 1354.00 1354.00 1354.00 1354.00 1354.00 1354.00
mean 676.50 2022-09-22 14:05:29.689808128 4078.79 4123.02 4036.61 4081.27 453494.47 0.06
min 0.00 2020-01-01 00:00:00 1934.98 2175.88 1900.73 1916.11 0.00 -13.37
25% 338.25 2021-05-12 12:00:00 3352.57 3381.77 3313.15 3353.21 235559.25 -0.65
50% 676.50 2022-09-20 12:00:00 3746.12 3785.10 3706.95 3759.01 347602.50 0.03
75% 1014.75 2024-02-04 06:00:00 4837.21 4878.36 4785.20 4838.45 524854.00 0.73
max 1353.00 2025-06-19 00:00:00 6446.05 6469.90 6306.90 6444.20 6750074.00 10.61
std 391.01 NaN 907.37 912.74 900.90 907.79 438221.06 1.50
In [31]:
plot_close_line(BRIT_df,"Britannia Industries Ltd. - Closing Price (₹)")
plot_financial_data(BRIT_df[['Date', 'Open', 'High', 'Low']], "Britannia Industries Ltd. - Stock Prices (₹)")
plot_financial_data(BRIT_df[['Date','Daily Return']], "Britannia Industries Ltd. – Daily Returns (%)")
plot_financial_data(BRIT_df[['Date','Volume']], "Britannia Industries Ltd. – Volume")
In [32]:
BRIT_df['Date'] = pd.to_datetime(BRIT_df['Date'])

# Set it as the index
BRIT_df.set_index('Date', inplace=True)

# Optional cleanup: remove unnamed index column if it exists
if 'index' in BRIT_df.columns:
    BRIT_df.drop('index', axis=1, inplace=True)

plot_candlestick_with_moving_averages(BRIT_df,
    title='Britania – Candlestick Chart with Moving Averages',
    ma_periods=[14, 21, 50])

Analysis :¶

  • Average Daily Return: ~0.10%
  • Volatility: ~1.5%
  • Britannia demonstrates higher short-term volatility due to input cost fluctuations (wheat, sugar, oil).
  • The company has strong brand loyalty and pricing power, helping to maintain margins.
  • Ideal for investors looking for a growth-oriented food segment stock with moderately high returns and sector leadership.

3. Comparative Performance Analysis of FMCG Stocks¶

In [33]:
tickers = ['HINDUNILVR.NS', 'NESTLEIND.NS', 'ITC.NS', 'DABUR.NS', 'BRITANNIA.NS']
start_date = '2020-01-01'
end_date = datetime.today().strftime('%Y-%m-%d')

# Download closing prices only
close_prices_df = yf.download(tickers, start=start_date, end=end_date)['Close']

# Optional: rename columns for readability
close_prices_df.columns = ['HUL', 'Nestle', 'ITC', 'Dabur', 'Britannia']

# Drop any rows with all NaNs (e.g., holidays)
close_prices_df.dropna(how='all', inplace=True)


close_prices_df

C:\Users\ashish gupta\AppData\Local\Temp\ipykernel_17084\3250086399.py:6: FutureWarning:

YF.download() has changed argument auto_adjust default to True

[*********************100%***********************]  5 of 5 completed
Out[33]:
HUL Nestle ITC Dabur Britannia
Date
2020-01-01 2751.210693 439.835175 1789.385864 188.254211 1394.457153
2020-01-02 2763.656006 439.978638 1790.771729 189.637894 1389.767822
2020-01-03 2750.441650 435.817596 1780.977173 188.570480 1376.959351
2020-01-06 2735.280762 428.547546 1769.889282 185.882248 1360.263428
2020-01-07 2748.449707 433.808716 1774.740112 186.079926 1348.384399
... ... ... ... ... ...
2025-06-13 5570.000000 466.500000 2319.000000 413.899994 2376.800049
2025-06-16 5560.500000 471.700012 2327.399902 418.200012 2389.800049
2025-06-17 5568.500000 472.049988 2328.600098 417.399994 2362.000000
2025-06-18 5572.500000 470.200012 2297.300049 415.899994 2340.800049
2025-06-19 5558.000000 465.100006 2294.199951 416.000000 2318.899902

1354 rows × 5 columns

In [34]:
daily_returns_df = close_prices_df.pct_change()
daily_returns_df.replace(np.nan, 0, inplace = True)
daily_returns_df
Out[34]:
HUL Nestle ITC Dabur Britannia
Date
2020-01-01 0.000000 0.000000 0.000000 0.000000 0.000000
2020-01-02 0.004524 0.000326 0.000774 0.007350 -0.003363
2020-01-03 -0.004781 -0.009457 -0.005469 -0.005629 -0.009216
2020-01-06 -0.005512 -0.016681 -0.006226 -0.014256 -0.012125
2020-01-07 0.004814 0.012277 0.002741 0.001063 -0.008733
... ... ... ... ... ...
2025-06-13 0.000090 -0.013638 -0.005873 -0.016865 -0.003689
2025-06-16 -0.001706 0.011147 0.003622 0.010389 0.005470
2025-06-17 0.001439 0.000742 0.000516 -0.001913 -0.011633
2025-06-18 0.000718 -0.003919 -0.013442 -0.003594 -0.008975
2025-06-19 -0.002602 -0.010846 -0.001349 0.000240 -0.009356

1354 rows × 5 columns

In [35]:
# Function to scale stock prices based on their initial starting price
# The objective of this function is to set all prices to start at a value of 1 
def price_scaling(raw_prices_df):
    scaled_prices_df = raw_prices_df.copy()
    for i in raw_prices_df.columns[:]:
          scaled_prices_df[i] = raw_prices_df[i]/raw_prices_df[i][0]
    return scaled_prices_df
In [36]:
scaled_price_df = price_scaling(close_prices_df)
scaled_price_df

C:\Users\ashish gupta\AppData\Local\Temp\ipykernel_17084\1220884393.py:6: FutureWarning:

Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
Out[36]:
HUL Nestle ITC Dabur Britannia
Date
2020-01-01 1.000000 1.000000 1.000000 1.000000 1.000000
2020-01-02 1.004524 1.000326 1.000774 1.007350 0.996637
2020-01-03 0.999720 0.990866 0.995301 1.001680 0.987452
2020-01-06 0.994210 0.974337 0.989104 0.987400 0.975479
2020-01-07 0.998996 0.986298 0.991815 0.988450 0.966960
... ... ... ... ... ...
2025-06-13 2.024563 1.060625 1.295975 2.198623 1.704463
2025-06-16 2.021110 1.072447 1.300670 2.221464 1.713785
2025-06-17 2.024018 1.073243 1.301340 2.217215 1.693849
2025-06-18 2.025472 1.069037 1.283848 2.209247 1.678646
2025-06-19 2.020202 1.057442 1.282116 2.209778 1.662941

1354 rows × 5 columns

In [37]:
plot_financial_data(close_prices_df.reset_index(), "Closing Prices of FMCG Stocks (₹) (Actual)")
In [38]:
plot_financial_data(scaled_price_df.reset_index(), "Closing Prices of FMCG Stocks (₹) (Scaled)")
In [39]:
plot_financial_data(daily_returns_df.reset_index(), "Daily Returns of FMCG Stocks (%)")
In [40]:
fig = px.histogram(daily_returns_df, 
                   barmode='overlay', 
                   opacity=0.5,
                   nbins=400)

fig.update_layout(
    title='Histogram of Daily Returns for FMCG Stocks',
    xaxis_title='Daily Return (%)',
    yaxis_title='Frequency',
    plot_bgcolor='white',
    legend_title_text='Company'
)

pyo.iplot(fig)
In [41]:
plt.figure(figsize=(10, 8))
sns.heatmap(daily_returns_df.corr(), annot=True,fmt=".2f")
plt.title("Correlation Heatmap of Daily Returns")
plt.show()
No description has been provided for this image
In [42]:
sns.pairplot(daily_returns_df, height=2, aspect=1.2)
Out[42]:
<seaborn.axisgrid.PairGrid at 0x12b0f68f4d0>
No description has been provided for this image

Comparative Analysis Summary:¶

The FMCG sector analysis reveals that the selected companies offer relatively stable returns with low volatility, consistent with the sector's defensive nature. For example, stocks like Hindustan Unilever and Nestlé India exhibited strong performance with less fluctuation, while still contributing meaningfully to portfolio returns. The correlation matrix among stocks showed moderate interdependence, indicating some diversification benefits within the sector. Overall, this sector remains a low-risk, steady-growth option ideal for conservative investment strategies.

In [ ]:

4. Value at Risk (VaR) Analysis¶

Historical method¶

In [43]:
#Creating an equally weighted portfolio
tickers = ['HINDUNILVR.NS', 'NESTLEIND.NS', 'ITC.NS', 'DABUR.NS', 'BRITANNIA.NS']
# Assuming an initial portfolio value of 1,000,000 and equal weights for each stock 
portfolio_value = 1000000
weights = np.array([1/len(tickers)]*len(tickers))
print(weights)

[0.2 0.2 0.2 0.2 0.2]
In [44]:
historical_returns = (daily_returns_df * weights).sum(axis=1)
historical_returns
Out[44]:
Date
2020-01-01    0.000000
2020-01-02    0.001922
2020-01-03   -0.006911
2020-01-06   -0.010960
2020-01-07    0.002433
                ...   
2025-06-13   -0.007995
2025-06-16    0.005784
2025-06-17   -0.002170
2025-06-18   -0.005842
2025-06-19   -0.004783
Length: 1354, dtype: float64
In [45]:
# Calculate Value at Risk (VaR) at 95% confidence level
# VaR is the maximum loss not exceeded with a given confidence level over a specified period
confidence_interval = 0.95
VaR = -np.percentile(historical_returns, 100 - (confidence_interval * 100)) * portfolio_value

print(f"Portfolio VaR at 95% confidence: ₹{VaR:,.2f}")

Portfolio VaR at 95% confidence: ₹14,116.28
In [46]:
return_window = 1  

# Convert returns to dollar values
historical_returns_dollar = historical_returns * portfolio_value

# Plot histogram
plt.figure(figsize=(10, 6))
plt.hist(historical_returns_dollar.dropna(), bins=100, density=True, color='skyblue', edgecolor='black')
plt.xlabel('1-Day Portfolio Return (₹)')
plt.ylabel('Density')
plt.title(f'Distribution of Portfolio {return_window}-Day Returns (₹)')

# Add vertical VaR line
plt.axvline(-VaR, color='red', linestyle='dashed', linewidth=2, label=f'VaR at {confidence_interval:.0%} confidence level')

plt.legend()
plt.grid(True, linestyle='--', alpha=0.5)
plt.tight_layout()
plt.show()
No description has been provided for this image

Montecarlo Simulation¶

In [47]:
#function to calculate expected return of a portfolio
def expected_return(weights, log_returns):
    return np.sum(log_returns.mean()*weights)
In [48]:
def standard_deviation (weights, cov_matrix):
    variance = weights.T @ cov_matrix @ weights
    return np.sqrt(variance)
In [49]:
Cov_matrix = daily_returns_df.cov()
Cov_matrix
Out[49]:
HUL Nestle ITC Dabur Britannia
HUL 0.000225 0.000108 0.000128 0.000075 0.000118
Nestle 0.000108 0.000210 0.000120 0.000077 0.000094
ITC 0.000128 0.000120 0.000221 0.000072 0.000124
Dabur 0.000075 0.000077 0.000072 0.000257 0.000055
Britannia 0.000118 0.000094 0.000124 0.000055 0.000195
In [50]:
portfolio_expected_return = expected_return(weights, daily_returns_df)
portfolio_std_dev = standard_deviation(weights, Cov_matrix)
In [51]:
# Function to generate a random z-score for Monte Carlo simulation
def random_z_score():
    return np.random.normal(0,1)
In [52]:
# Function to calculate gain/loss for a given portfolio value, expected return, standard deviation, z-score, and number of days
portfolio_value = 1000000  # Example portfolio value
days = 1
def scenario_gain_loss(portfolio_value, portfolio_std_dev, z_score, days):
    return portfolio_value * portfolio_expected_return * days + portfolio_value * portfolio_std_dev * z_score * np.sqrt(days)
In [53]:
# Monte Carlo simulation to generate scenario returns
simulations = 10000
scenarioReturn =[]

for i in range(simulations):
    z_score = random_z_score()
    scenarioReturn.append(scenario_gain_loss(portfolio_value, portfolio_std_dev, z_score, days))
In [54]:
confidence_interval = 0.95
VaR_M = -np.percentile(scenarioReturn, 100*(1- confidence_interval))
print(f"Portfolio VaR at 95% confidence: ₹{VaR_M:,.2f}")

Portfolio VaR at 95% confidence: ₹18,125.66
In [55]:
return_window = 1  

# Plot histogram
plt.figure(figsize=(10, 6))
plt.hist(scenarioReturn, bins=100, density=True, color='skyblue', edgecolor='black')
plt.xlabel('1-Day Portfolio Return (₹)')
plt.ylabel('Density')
plt.title(f'Distribution of Portfolio {return_window}-Day Returns (₹)')

# Add vertical VaR line
plt.axvline(-VaR, color='red', linestyle='dashed', linewidth=2, label=f'VaR at {confidence_interval:.0%} confidence level')

plt.legend()
plt.grid(True, linestyle='--', alpha=0.5)
plt.tight_layout()
plt.show()
No description has been provided for this image

Value at Risk (VaR) Analysis Summary:¶

Two methods were applied to estimate potential losses:

  • The Historical VaR method at a 95% confidence level estimated the maximum daily portfolio loss to be approximately ₹14,120.58
  • The Montecarlo Simulation VaR method at a 95% confidence level estimated the maximum daily portfolio loss to be approximately ₹17,855.86
  • The Monte Carlo Simulation and Historical methods projected similar risk levels using portfolio mean return and volatility, accounting for random market fluctuations.

Disclaimer¶

This project is intended for educational and academic purposes only, and is not intended as financial or investment advice. It uses historical data and methodologies for demonstration, and past performance is not indicative of future results. Investors should always conduct their own research and consult qualified professionals before making investment decisions.